Outlier Detection in Wireless Sensor Networks Using Machine Learning and Statistical Based Approaches
Bibliographic record
Abstract
Outliers in wireless sensor networks (WSNs), stemming from harsh environmental conditions and limited processing and communication capacities of sensor nodes, pose a significant challenge to data reliability and quality collected by the network.Energyefficient outlier detection methods are crucial for prolonging network lifespan.This study introduces a two-phase approach to address this challenge.At sensor nodes, a lightweight statistical method based on mean and standard deviation detects and removes outliers, conserving energy.Early outlier filtering reduces data transmission, saving substantial energy due to the high energy cost of communication in WSNs.At the base station, several unsupervised Machine Learning algorithms, including One Class Support Vector Machine (OCSVM), Histogram Based Outlier Score (HBOS), Isolation Forest (IForest), K-Nearest Neighbor (KNN), and Cluster Based Local Outlier Factor (CBLOF), identify remaining outliers.The base station, with greater computational power and energy resources, can handle these tasks without the constraints faced by sensor nodes.Evaluation using realworld datasets demonstrates the effectiveness of our approach, achieving a 77.59% outlier removal rate at the node level while maintaining over 90% detection accuracy at the base station.By employing computationally light statistical methods at sensor nodes, reducing data transmission, and shifting complex tasks to the base station, our approach optimizes energy efficiency, minimizing consumption and reducing the need for frequent recalibration or maintenance, thereby extending the lifespan of the network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".